AI safety & Security

Apart Research

Artificial intelligence will change the world. Our mission is to ensure this happens safely and to the benefit of everyone.

AI safety needs to scale, and here's how you can do it

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Apart > Research

Foundational Research for Safe and Beneficial Advanced AI

Apart > Sprints

Global Hackathons and Challenges in AI Safety for Aspiring Researchers

Apart > Lab

Incubating Talented Research Teams Towards Real-World Impact

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Our Published Papers

Foundational AI Safety Research

We aim to produce foundational research enabling the safe and beneficial development of advanced AI. See our publications below.

Luke Marks, Amir Abdullah, Luna Mendez, Rauno Arike, Philip Torr, Fazl Barez

arXiv
[Read Publication}

Interpreting Reward Models in RLHF-Tuned Language Models Using Sparse Autoencoders

Alex Foote1*, Neel Nanda2, Esben Kran1, Ionnis Konstas3, Shay Cohen4, Fazl Barez1,4,5*

RTML workshop at ICLR 2023
[Read Publication}

Neuron to Graph: Interpreting Language Model Neurons at Scale

NeurIPS 2023 XAI in Action Workshop
[Read Publication}

DeepDecipher

Philip Quirke, Fazl Barez

arXiv
[Read Publication}

Understanding Addition in Transformers

Michael Lan, Fazl Barez

arXiv
[Read Publication}

Locating Cross-Task Sequence Continuation Circuits in Transformers

Detecting Edit Failures In Large Language Models: An Improved Specificity Benchmark

Jason Hoelscher-Obermaier1*, Julia Persson1*, Esben Kran1, Ionnis Konstas2, Fazl Barez1,3*

ACL 2023
[Read Publication}

The Larger They Are, the Harder They Fail: Language Models do not Recognize Identifier Swaps in Python

Antonio Valerio Miceli-Barone1*, Fazl Barez1*, Ioannis Konstas2, Shay B. Cohen1

ACL 2023
[Read Publication}
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Next Research Sprint

Code Red Hackathon: Can LLMs Replicate by Themselves?

This event concluded on
Mar 25, 2024
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Events

22
Mar

Code Red Hackathon: Can LLMs Replicate by Themselves?

Virtual & in-person participation
22
Mar

Code Red Hackathon: Can LLMs Replicate by Themselves?

Virtual & in-person participation
5
Apr

LLM Psychology Hackathon

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5
Apr

LLM Psychology Hackathon

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26
Apr

AI Governance Research Sprint

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26
Apr

AI Governance Research Sprint

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24
May

Model Evaluation Hackathon

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24
May

Model Evaluation Hackathon

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What does Apart do?

We solve high-impact, neglected and tractable problems in AI safety

Field-building for AI safety

Our initiatives allow people from diverse backgrounds to have an impact on AI safety. On 7 continents, more than 200 projects have been developed by over 1,000 researchers. Some teams have gone on to publish their research at major academic venues, such as NeurIPS and ACL.

High-impact research

We engage in both original research and contracting for research projects that aim to translate academic insights into actionable strategies for mitigating catastrophic risks associated with AI. We have co-authored with researchers from the University of Oxford, DeepMind, Edinburgh University and more.

A vision for the future

Our aim is to foster a positive vision and an action-focused approach to AI safety, a commitment underscored by our signing of both the Statement on AI Risk and the Letter for an AI Moratorium. We are privileged to not only be tightly connected with but also actively develop a large community in AI safety.

Apart Research

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Check out the list below for ways you can interact or research with Apart!

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Commanding Research Executive
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Head of Climate Research Associations
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Associate Professor Formula T.
Honorary Associate Fellow of Research Ethics and Linguistics
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Commander of the Internally Restricted CINeMa Research
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